Data Analysis Climate Law Study
Nina Frings & Julius Fenn
2023-08-31
Analysis
Descriptive statistics sample
Participants in first wave: 351
Participants that completed both wavves: 254
Additional “control group” partcipants: 72
percentage of overall sample that completed wave 1 and wave 2
| divers | Keine Angabe | männlich | weiblich | |
|---|---|---|---|---|
| 18-29 | 0 | 0 | 14 | 8 |
| 30-39 | 0 | 0 | 12 | 10 |
| 40-49 | 0 | 0 | 10 | 7 |
| 50-59 | 0 | 0 | 11 | 13 |
| 60-80 | 0 | 0 | 11 | 4 |
Political party identification
Media, climate change concern
Climate change concern
Cronbachs’s alpha of 0.95, skew of -0.99
By party
Media engagement
The items were phrased as: How did you inform yourself about the climate law and the vote in the past month
Cronbachs alpha of 0.89
Type of media used as information source
News media
Newspapers used as source of information
Correlations between predictor variables cc concern, rating of law and political orientation:
CAM Indicators
Did people change the valence of “Klimagesetz”?
| t1 | t2 | wave2_control | |
|---|---|---|---|
| -3 | 11 | 3 | 2 |
| -2 | 2 | 5 | 1 |
| -1 | 4 | 2 | 0 |
| 0 | 207 | 223 | 60 |
| 1 | 8 | 8 | 2 |
| 2 | 14 | 6 | 3 |
| 3 | 7 | 6 | 4 |
Macro indicators
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Mean SD Median CoeffofVariation
## mean_valence_macro_t1 0.11 0.94 0.11 8.61
## mean_valence_normed_macro_t1 0.06 0.38 0.00 6.60
## density_macro_t1 0.27 0.09 0.25 0.33
## transitivity_macro_t1 0.14 0.18 0.00 1.26
## centr_degree_macro_t1 0.47 0.23 0.42 0.50
## centr_clo_macro_t1 0.61 0.23 0.56 0.38
## centr_betw_macro_t1 0.66 0.23 0.67 0.35
## centr_eigen_macro_t1 0.63 0.10 0.65 0.16
## meanDistance_directed_macro_t1 2.11 0.41 2.06 0.20
## meanDistance_undirected_macro_t1 2.11 0.41 2.06 0.20
## diameter_weighted_undirected_macro_t1 3.54 1.23 4.00 0.35
## diameter_unweighted_undirected_macro_t1 3.54 1.23 4.00 0.35
## diameter_unweighted_directed_macro_t1 3.54 1.23 4.00 0.35
## num_nodes_macro_t1 9.63 2.18 9.00 0.23
## num_nodes_pos_macro_t1 3.75 2.35 4.00 0.63
## num_nodes_neg_macro_t1 3.21 2.38 3.00 0.74
## num_nodes_neut_macro_t1 1.67 1.75 1.00 1.04
## num_nodes_ambi_macro_t1 1.00 1.31 1.00 1.31
## num_edges_macro_t1 21.46 7.37 20.00 0.34
## num_edges_solid_macro_t1 21.46 7.37 20.00 0.34
## num_edges_dashed_macro_t1 0.00 0.00 0.00 NaN
## num_edges_invaliddashed_macro_t1 1.08 1.65 0.00 1.53
## meanWeightEdges_macro_t1 1.00 0.00 1.00 0.00
## reciprocity_macro_t1 1.00 0.00 1.00 0.00
## assortativity_valence_macro_t1 -0.21 0.30 -0.20 -1.46
## assortativityDegree_macro_t1 -0.52 0.28 -0.51 -0.54
## Minimum Maximun Lower Quantile
## mean_valence_macro_t1 -3.00 2.88 -3.00
## mean_valence_normed_macro_t1 -1.00 1.00 -1.00
## density_macro_t1 0.08 0.64 0.08
## transitivity_macro_t1 0.00 0.69 0.00
## centr_degree_macro_t1 0.00 0.89 0.00
## centr_clo_macro_t1 0.00 1.00 0.00
## centr_betw_macro_t1 0.00 1.00 0.00
## centr_eigen_macro_t1 0.00 0.79 0.00
## meanDistance_directed_macro_t1 1.36 3.67 1.36
## meanDistance_undirected_macro_t1 1.36 3.67 1.36
## diameter_weighted_undirected_macro_t1 2.00 8.00 2.00
## diameter_unweighted_undirected_macro_t1 2.00 8.00 2.00
## diameter_unweighted_directed_macro_t1 2.00 8.00 2.00
## num_nodes_macro_t1 8.00 27.00 8.00
## num_nodes_pos_macro_t1 0.00 22.00 0.00
## num_nodes_neg_macro_t1 0.00 17.00 0.00
## num_nodes_neut_macro_t1 0.00 10.00 0.00
## num_nodes_ambi_macro_t1 0.00 8.00 0.00
## num_edges_macro_t1 14.00 56.00 14.00
## num_edges_solid_macro_t1 14.00 56.00 14.00
## num_edges_dashed_macro_t1 0.00 0.00 0.00
## num_edges_invaliddashed_macro_t1 0.00 9.00 0.00
## meanWeightEdges_macro_t1 1.00 1.00 1.00
## reciprocity_macro_t1 1.00 1.00 1.00
## assortativity_valence_macro_t1 -1.00 0.59 -1.00
## assortativityDegree_macro_t1 -1.00 0.13 -1.00
## Upper Quantile Skewness Kurtosis(-3)
## mean_valence_macro_t1 2.88 -0.63 1.67
## mean_valence_normed_macro_t1 1.00 -0.33 0.53
## density_macro_t1 0.64 1.37 2.49
## transitivity_macro_t1 0.69 0.98 -0.22
## centr_degree_macro_t1 0.89 0.33 -1.08
## centr_clo_macro_t1 1.00 0.23 -1.00
## centr_betw_macro_t1 1.00 -0.37 -0.46
## centr_eigen_macro_t1 0.79 -1.52 4.74
## meanDistance_directed_macro_t1 3.67 0.87 0.81
## meanDistance_undirected_macro_t1 3.67 0.87 0.81
## diameter_weighted_undirected_macro_t1 8.00 0.58 0.09
## diameter_unweighted_undirected_macro_t1 8.00 0.58 0.09
## diameter_unweighted_directed_macro_t1 8.00 0.58 0.09
## num_nodes_macro_t1 27.00 3.88 21.49
## num_nodes_pos_macro_t1 22.00 2.15 14.10
## num_nodes_neg_macro_t1 17.00 1.53 5.06
## num_nodes_neut_macro_t1 10.00 2.28 6.17
## num_nodes_ambi_macro_t1 8.00 2.42 8.78
## num_edges_macro_t1 56.00 1.94 4.75
## num_edges_solid_macro_t1 56.00 1.94 4.75
## num_edges_dashed_macro_t1 0.00 NaN NaN
## num_edges_invaliddashed_macro_t1 9.00 1.94 3.85
## meanWeightEdges_macro_t1 1.00 NaN NaN
## reciprocity_macro_t1 1.00 NaN NaN
## assortativity_valence_macro_t1 0.59 -0.06 -0.15
## assortativityDegree_macro_t1 0.13 -0.01 -0.69
## KS-Test
## mean_valence_macro_t1 0.01
## mean_valence_normed_macro_t1 0.00
## density_macro_t1 0.00
## transitivity_macro_t1 0.00
## centr_degree_macro_t1 0.01
## centr_clo_macro_t1 0.00
## centr_betw_macro_t1 0.17
## centr_eigen_macro_t1 0.01
## meanDistance_directed_macro_t1 0.02
## meanDistance_undirected_macro_t1 0.02
## diameter_weighted_undirected_macro_t1 0.00
## diameter_unweighted_undirected_macro_t1 0.00
## diameter_unweighted_directed_macro_t1 0.00
## num_nodes_macro_t1 0.00
## num_nodes_pos_macro_t1 0.00
## num_nodes_neg_macro_t1 0.00
## num_nodes_neut_macro_t1 0.00
## num_nodes_ambi_macro_t1 0.00
## num_edges_macro_t1 0.00
## num_edges_solid_macro_t1 0.00
## num_edges_dashed_macro_t1 0.00
## num_edges_invaliddashed_macro_t1 0.00
## meanWeightEdges_macro_t1 0.00
## reciprocity_macro_t1 0.00
## assortativity_valence_macro_t1 0.98
## assortativityDegree_macro_t1 0.21Micro indicators
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Mean SD Median CoeffofVariation Minimum
## degreetot_micro_Klimagesetz_t1 10.45 4.16 10 0.40 2.0
## valence_micro_Klimagesetz_t1 0.06 0.97 0 17.32 -3.0
## centr_clo_micro_Klimagesetz_t1 0.96 0.10 1 0.10 0.5
## Maximun Lower Quantile Upper Quantile Skewness
## degreetot_micro_Klimagesetz_t1 22 2.0 22 0.10
## valence_micro_Klimagesetz_t1 3 -3.0 3 -0.24
## centr_clo_micro_Klimagesetz_t1 1 0.5 1 -2.88
## Kurtosis(-3) KS-Test
## degreetot_micro_Klimagesetz_t1 -0.81 0
## valence_micro_Klimagesetz_t1 4.93 0
## centr_clo_micro_Klimagesetz_t1 8.04 0For difference values
Macro indicators
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Mean SD Median CoeffofVariation
## mean_valence_macro_t1 0.00 0.80 0.00 -267.78
## mean_valence_normed_macro_t1 0.00 0.35 0.00 85.89
## density_macro_t1 -0.01 0.11 0.00 -12.57
## transitivity_macro_t1 -0.01 0.21 0.00 -19.07
## centr_degree_macro_t1 -0.01 0.26 0.00 -21.79
## centr_clo_macro_t1 -0.01 0.27 0.00 -48.97
## centr_betw_macro_t1 0.00 0.28 0.00 363.15
## centr_eigen_macro_t1 0.01 0.15 0.00 10.01
## meanDistance_directed_macro_t1 -0.01 0.51 0.00 -36.60
## meanDistance_undirected_macro_t1 -0.01 0.51 0.00 -36.60
## diameter_weighted_undirected_macro_t1 -0.03 1.59 0.00 -51.00
## diameter_unweighted_undirected_macro_t1 0.04 1.45 0.00 33.76
## diameter_unweighted_directed_macro_t1 0.04 1.45 0.00 33.76
## num_nodes_macro_t1 -0.08 2.53 0.00 -32.35
## num_nodes_pos_macro_t1 0.04 2.57 0.00 59.71
## num_nodes_neg_macro_t1 0.12 2.20 0.00 18.18
## num_nodes_neut_macro_t1 -0.48 1.91 0.00 -3.97
## num_nodes_ambi_macro_t1 0.24 1.47 0.00 6.18
## num_edges_macro_t1 -0.73 8.04 0.00 -11.07
## num_edges_solid_macro_t1 -0.73 8.04 0.00 -11.07
## num_edges_dashed_macro_t1 0.00 0.00 0.00 NaN
## num_edges_invaliddashed_macro_t1 0.04 1.91 0.00 44.46
## meanWeightEdges_macro_t1 -0.02 0.10 0.00 -5.57
## reciprocity_macro_t1 0.00 0.00 0.00 NaN
## assortativity_valence_macro_t1 0.03 0.38 0.06 11.03
## assortativityDegree_macro_t1 0.04 0.32 0.01 7.22
## mean_valence_macro_t2 0.12 1.01 0.09 8.72
## mean_valence_normed_macro_t2 0.05 0.37 0.00 6.90
## density_macro_t2 0.28 0.11 0.25 0.41
## transitivity_macro_t2 0.15 0.20 0.00 1.28
## centr_degree_macro_t2 0.48 0.26 0.45 0.53
## centr_clo_macro_t2 0.62 0.26 0.59 0.42
## centr_betw_macro_t2 0.66 0.26 0.68 0.39
## centr_eigen_macro_t2 0.62 0.13 0.65 0.22
## meanDistance_directed_macro_t2 2.12 0.53 1.96 0.25
## meanDistance_undirected_macro_t2 2.12 0.53 1.96 0.25
## diameter_weighted_undirected_macro_t2 3.58 1.51 3.00 0.42
## diameter_unweighted_undirected_macro_t2 3.50 1.38 3.00 0.39
## diameter_unweighted_directed_macro_t2 3.50 1.38 3.00 0.39
## num_nodes_macro_t2 9.70 2.49 9.00 0.26
## num_nodes_pos_macro_t2 3.70 2.52 4.00 0.68
## num_nodes_neg_macro_t2 3.08 2.12 3.00 0.69
## num_nodes_neut_macro_t2 2.16 2.28 1.00 1.06
## num_nodes_ambi_macro_t2 0.77 1.17 0.00 1.53
## num_edges_macro_t2 22.16 8.30 20.00 0.37
## num_edges_solid_macro_t2 22.16 8.30 20.00 0.37
## num_edges_dashed_macro_t2 0.00 0.00 0.00 NaN
## num_edges_invaliddashed_macro_t2 1.03 1.80 0.00 1.74
## meanWeightEdges_macro_t2 1.02 0.10 1.00 0.09
## reciprocity_macro_t2 1.00 0.00 1.00 0.00
## assortativity_valence_macro_t2 -0.24 0.31 -0.22 -1.31
## assortativityDegree_macro_t2 -0.56 0.29 -0.56 -0.51
## Minimum Maximun Lower Quantile
## mean_valence_macro_t1 -3.36 3.07 -3.36
## mean_valence_normed_macro_t1 -1.77 1.14 -1.77
## density_macro_t1 -0.76 0.31 -0.76
## transitivity_macro_t1 -0.96 0.60 -0.96
## centr_degree_macro_t1 -0.88 0.73 -0.88
## centr_clo_macro_t1 -1.00 0.78 -1.00
## centr_betw_macro_t1 -1.00 0.80 -1.00
## centr_eigen_macro_t1 -0.74 0.60 -0.74
## meanDistance_directed_macro_t1 -3.08 1.51 -3.08
## meanDistance_undirected_macro_t1 -3.08 1.51 -3.08
## diameter_weighted_undirected_macro_t1 -8.00 5.00 -8.00
## diameter_unweighted_undirected_macro_t1 -5.00 5.00 -5.00
## diameter_unweighted_directed_macro_t1 -5.00 5.00 -5.00
## num_nodes_macro_t1 -15.00 10.00 -15.00
## num_nodes_pos_macro_t1 -12.00 10.00 -12.00
## num_nodes_neg_macro_t1 -6.00 17.00 -6.00
## num_nodes_neut_macro_t1 -11.00 5.00 -11.00
## num_nodes_ambi_macro_t1 -7.00 8.00 -7.00
## num_edges_macro_t1 -32.00 24.00 -32.00
## num_edges_solid_macro_t1 -32.00 24.00 -32.00
## num_edges_dashed_macro_t1 0.00 0.00 0.00
## num_edges_invaliddashed_macro_t1 -7.00 7.00 -7.00
## meanWeightEdges_macro_t1 -1.00 0.00 -1.00
## reciprocity_macro_t1 0.00 0.00 0.00
## assortativity_valence_macro_t1 -1.00 0.97 -1.00
## assortativityDegree_macro_t1 -1.12 1.02 -1.12
## mean_valence_macro_t2 -2.73 3.00 -2.73
## mean_valence_normed_macro_t2 -0.91 1.00 -0.91
## density_macro_t2 0.08 0.96 0.08
## transitivity_macro_t2 0.00 0.96 0.00
## centr_degree_macro_t2 0.03 0.89 0.03
## centr_clo_macro_t2 0.04 1.00 0.04
## centr_betw_macro_t2 0.00 1.00 0.00
## centr_eigen_macro_t2 0.04 0.81 0.04
## meanDistance_directed_macro_t2 1.04 4.83 1.04
## meanDistance_undirected_macro_t2 1.04 4.83 1.04
## diameter_weighted_undirected_macro_t2 2.00 10.00 2.00
## diameter_unweighted_undirected_macro_t2 2.00 10.00 2.00
## diameter_unweighted_directed_macro_t2 2.00 10.00 2.00
## num_nodes_macro_t2 8.00 27.00 8.00
## num_nodes_pos_macro_t2 0.00 18.00 0.00
## num_nodes_neg_macro_t2 0.00 10.00 0.00
## num_nodes_neut_macro_t2 0.00 14.00 0.00
## num_nodes_ambi_macro_t2 0.00 7.00 0.00
## num_edges_macro_t2 14.00 56.00 14.00
## num_edges_solid_macro_t2 14.00 56.00 14.00
## num_edges_dashed_macro_t2 0.00 0.00 0.00
## num_edges_invaliddashed_macro_t2 0.00 10.00 0.00
## meanWeightEdges_macro_t2 1.00 2.00 1.00
## reciprocity_macro_t2 1.00 1.00 1.00
## assortativity_valence_macro_t2 -1.00 0.71 -1.00
## assortativityDegree_macro_t2 -1.00 0.41 -1.00
## Upper Quantile Skewness Kurtosis(-3)
## mean_valence_macro_t1 3.07 -0.29 2.10
## mean_valence_normed_macro_t1 1.14 -0.42 2.48
## density_macro_t1 0.31 -1.74 8.76
## transitivity_macro_t1 0.60 -0.64 2.29
## centr_degree_macro_t1 0.73 -0.35 0.80
## centr_clo_macro_t1 0.78 -0.32 0.93
## centr_betw_macro_t1 0.80 -0.13 0.88
## centr_eigen_macro_t1 0.60 0.19 3.43
## meanDistance_directed_macro_t1 1.51 -1.26 7.38
## meanDistance_undirected_macro_t1 1.51 -1.26 7.38
## diameter_weighted_undirected_macro_t1 5.00 -0.71 3.28
## diameter_unweighted_undirected_macro_t1 5.00 -0.07 1.20
## diameter_unweighted_directed_macro_t1 5.00 -0.07 1.20
## num_nodes_macro_t1 10.00 -1.04 10.01
## num_nodes_pos_macro_t1 10.00 -0.09 3.58
## num_nodes_neg_macro_t1 17.00 2.02 13.56
## num_nodes_neut_macro_t1 5.00 -1.48 5.69
## num_nodes_ambi_macro_t1 8.00 0.64 7.80
## num_edges_macro_t1 24.00 -0.78 3.12
## num_edges_solid_macro_t1 24.00 -0.78 3.12
## num_edges_dashed_macro_t1 0.00 NaN NaN
## num_edges_invaliddashed_macro_t1 7.00 -0.01 3.80
## meanWeightEdges_macro_t1 0.00 -7.20 58.10
## reciprocity_macro_t1 0.00 NaN NaN
## assortativity_valence_macro_t1 0.97 -0.14 -0.24
## assortativityDegree_macro_t1 1.02 -0.15 1.30
## mean_valence_macro_t2 3.00 -0.41 1.24
## mean_valence_normed_macro_t2 1.00 -0.27 0.69
## density_macro_t2 0.96 2.14 7.29
## transitivity_macro_t2 0.96 1.18 0.80
## centr_degree_macro_t2 0.89 0.22 -1.29
## centr_clo_macro_t2 1.00 0.10 -1.14
## centr_betw_macro_t2 1.00 -0.44 -0.62
## centr_eigen_macro_t2 0.81 -1.38 1.75
## meanDistance_directed_macro_t2 4.83 1.44 3.31
## meanDistance_undirected_macro_t2 4.83 1.44 3.31
## diameter_weighted_undirected_macro_t2 10.00 1.20 1.97
## diameter_unweighted_undirected_macro_t2 10.00 0.97 1.40
## diameter_unweighted_directed_macro_t2 10.00 0.97 1.40
## num_nodes_macro_t2 27.00 3.90 19.62
## num_nodes_pos_macro_t2 18.00 1.24 4.36
## num_nodes_neg_macro_t2 10.00 0.67 0.27
## num_nodes_neut_macro_t2 14.00 2.33 5.70
## num_nodes_ambi_macro_t2 7.00 2.13 5.65
## num_edges_macro_t2 56.00 1.69 3.12
## num_edges_solid_macro_t2 56.00 1.69 3.12
## num_edges_dashed_macro_t2 0.00 NaN NaN
## num_edges_invaliddashed_macro_t2 10.00 2.34 5.87
## meanWeightEdges_macro_t2 2.00 7.20 58.10
## reciprocity_macro_t2 1.00 NaN NaN
## assortativity_valence_macro_t2 0.71 0.00 -0.24
## assortativityDegree_macro_t2 0.41 0.22 -0.33
## KS-Test
## mean_valence_macro_t1 0.16
## mean_valence_normed_macro_t1 0.11
## density_macro_t1 0.00
## transitivity_macro_t1 0.00
## centr_degree_macro_t1 0.00
## centr_clo_macro_t1 0.01
## centr_betw_macro_t1 0.14
## centr_eigen_macro_t1 0.04
## meanDistance_directed_macro_t1 0.05
## meanDistance_undirected_macro_t1 0.05
## diameter_weighted_undirected_macro_t1 0.00
## diameter_unweighted_undirected_macro_t1 0.00
## diameter_unweighted_directed_macro_t1 0.00
## num_nodes_macro_t1 0.00
## num_nodes_pos_macro_t1 0.00
## num_nodes_neg_macro_t1 0.00
## num_nodes_neut_macro_t1 0.00
## num_nodes_ambi_macro_t1 0.00
## num_edges_macro_t1 0.00
## num_edges_solid_macro_t1 0.00
## num_edges_dashed_macro_t1 0.00
## num_edges_invaliddashed_macro_t1 0.00
## meanWeightEdges_macro_t1 0.00
## reciprocity_macro_t1 0.00
## assortativity_valence_macro_t1 0.94
## assortativityDegree_macro_t1 0.35
## mean_valence_macro_t2 0.01
## mean_valence_normed_macro_t2 0.00
## density_macro_t2 0.00
## transitivity_macro_t2 0.00
## centr_degree_macro_t2 0.01
## centr_clo_macro_t2 0.01
## centr_betw_macro_t2 0.03
## centr_eigen_macro_t2 0.00
## meanDistance_directed_macro_t2 0.00
## meanDistance_undirected_macro_t2 0.00
## diameter_weighted_undirected_macro_t2 0.00
## diameter_unweighted_undirected_macro_t2 0.00
## diameter_unweighted_directed_macro_t2 0.00
## num_nodes_macro_t2 0.00
## num_nodes_pos_macro_t2 0.00
## num_nodes_neg_macro_t2 0.00
## num_nodes_neut_macro_t2 0.00
## num_nodes_ambi_macro_t2 0.00
## num_edges_macro_t2 0.00
## num_edges_solid_macro_t2 0.00
## num_edges_dashed_macro_t2 0.00
## num_edges_invaliddashed_macro_t2 0.00
## meanWeightEdges_macro_t2 0.00
## reciprocity_macro_t2 0.00
## assortativity_valence_macro_t2 0.84
## assortativityDegree_macro_t2 0.03Micro indicators
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Mean SD Median CoeffofVariation Minimum
## degreetot_micro_Klimagesetz_t1 -0.66 4.79 0 -7.21 -20.00
## valence_micro_Klimagesetz_t1 0.00 1.00 0 248.99 -6.00
## centr_clo_micro_Klimagesetz_t1 0.00 0.12 0 -25.48 -0.50
## degreetot_micro_Klimagesetz_t2 11.13 4.35 12 0.39 2.00
## valence_micro_Klimagesetz_t2 0.07 0.73 0 10.85 -3.00
## centr_clo_micro_Klimagesetz_t2 0.97 0.09 1 0.09 0.48
## Maximun Lower Quantile Upper Quantile Skewness
## degreetot_micro_Klimagesetz_t1 14.00 -20.00 14.00 -0.35
## valence_micro_Klimagesetz_t1 3.00 -6.00 3.00 -0.81
## centr_clo_micro_Klimagesetz_t1 0.52 -0.50 0.52 -0.12
## degreetot_micro_Klimagesetz_t2 24.00 2.00 24.00 -0.15
## valence_micro_Klimagesetz_t2 3.00 -3.00 3.00 0.70
## centr_clo_micro_Klimagesetz_t2 1.00 0.48 1.00 -3.35
## Kurtosis(-3) KS-Test
## degreetot_micro_Klimagesetz_t1 0.93 0
## valence_micro_Klimagesetz_t1 7.91 0
## centr_clo_micro_Klimagesetz_t1 5.39 0
## degreetot_micro_Klimagesetz_t2 -0.76 0
## valence_micro_Klimagesetz_t2 9.61 0
## centr_clo_micro_Klimagesetz_t2 11.32 0inferential statistics
Hypothesis 1
Emotional properties
H1
We expect to find differences in emotional properties of mental models of [those intending to vote yes versus no on the upcoming climate protection law]. More specifically, we assume that valence of yes-intention voters will be more positive and valence of no-intention voters will be more negative.
wave 1
we will perform linear regressions with the indicated mental model network indicators as DV and dummy coded group differences as IV (no/yes-intention, high/low climate concern, left/right political orientation). In the case of the latter two, we may also use a continuous DV to assess correlation with network properties. We will control for socio-demographic variables (gender, age, education).
With mean valence macro
Intended vote
Supported
| mean_valence_macro_t1 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -0.62 | -1.04 – -0.21 | 0.003 |
| intendedVote | 0.75 | 0.51 – 0.98 | <0.001 |
| age | 0.01 | -0.00 – 0.01 | 0.161 |
| gender [weiblich] | 0.05 | -0.17 – 0.27 | 0.659 |
| education [linear] | -0.22 | -0.41 – -0.03 | 0.024 |
| education [quadratic] | 0.08 | -0.11 – 0.28 | 0.411 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.154 / 0.137 | ||
| mean_valence_macro_t1 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -1.32 | -1.77 – -0.87 | <0.001 |
| ratingLaw | 0.22 | 0.17 – 0.27 | <0.001 |
| age | 0.01 | -0.00 – 0.01 | 0.076 |
| gender [weiblich] | 0.04 | -0.17 – 0.24 | 0.729 |
| education [linear] | -0.27 | -0.45 – -0.09 | 0.004 |
| education [quadratic] | 0.07 | -0.11 – 0.25 | 0.436 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.257 / 0.243 | ||
Climate change concern
Supported
| mean_valence_macro_t1 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -1.30 | -1.82 – -0.77 | <0.001 |
| climate concern | 0.29 | 0.20 – 0.37 | <0.001 |
| age | 0.00 | -0.01 – 0.01 | 0.617 |
| gender [weiblich] | 0.04 | -0.18 – 0.26 | 0.732 |
| education [linear] | -0.22 | -0.41 – -0.03 | 0.022 |
| education [quadratic] | 0.07 | -0.12 – 0.27 | 0.461 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.172 / 0.155 | ||
Political orientation
Supported (but when excluding climate concern and vote no longer)
| mean_valence_macro_t1 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.56 | 0.08 – 1.04 | 0.023 |
| politicalOrientation | -0.10 | -0.16 – -0.04 | 0.001 |
| age | 0.00 | -0.01 – 0.01 | 0.608 |
| gender [weiblich] | 0.03 | -0.21 – 0.26 | 0.831 |
| education [linear] | -0.22 | -0.42 – -0.02 | 0.033 |
| education [quadratic] | 0.14 | -0.07 – 0.35 | 0.180 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.059 / 0.040 | ||
All three
| mean_valence_macro_t1 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -1.23 | -1.94 – -0.52 | 0.001 |
| politicalOrientation | -0.01 | -0.07 – 0.06 | 0.819 |
| intendedVote | 0.44 | 0.16 – 0.72 | 0.002 |
| climate concern | 0.20 | 0.09 – 0.30 | <0.001 |
| age | 0.00 | -0.00 – 0.01 | 0.259 |
| gender [weiblich] | 0.04 | -0.18 – 0.25 | 0.742 |
| education [linear] | -0.23 | -0.42 – -0.05 | 0.015 |
| education [quadratic] | 0.07 | -0.12 – 0.26 | 0.496 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.206 / 0.183 | ||
| mean_valence_macro_t1 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -1.61 | -2.31 – -0.91 | <0.001 |
| politicalOrientation | 0.01 | -0.05 – 0.07 | 0.674 |
| ratingLaw | 0.19 | 0.12 – 0.26 | <0.001 |
| climate concern | 0.09 | -0.02 – 0.20 | 0.099 |
| age | 0.01 | -0.00 – 0.01 | 0.112 |
| gender [weiblich] | 0.04 | -0.17 – 0.25 | 0.723 |
| education [linear] | -0.26 | -0.44 – -0.08 | 0.004 |
| education [quadratic] | 0.06 | -0.12 – 0.24 | 0.509 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.266 / 0.245 | ||
wave 2
No major differences to wave 1 results
Intended vote & rating of law
Supported
| mean_valence_macro_t2 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -0.80 | -1.22 – -0.37 | <0.001 |
| intendedVote | 0.95 | 0.71 – 1.19 | <0.001 |
| age | 0.01 | -0.00 – 0.01 | 0.123 |
| gender [weiblich] | 0.12 | -0.11 – 0.35 | 0.324 |
| education [linear] | -0.13 | -0.32 – 0.07 | 0.207 |
| education [quadratic] | 0.12 | -0.08 – 0.32 | 0.241 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.214 / 0.198 | ||
| mean_valence_macro_t2 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -1.59 | -2.03 – -1.16 | <0.001 |
| ratingLaw | 0.26 | 0.22 – 0.31 | <0.001 |
| age | 0.01 | -0.00 – 0.01 | 0.066 |
| gender [weiblich] | 0.11 | -0.10 – 0.32 | 0.308 |
| education [linear] | -0.12 | -0.30 – 0.06 | 0.197 |
| education [quadratic] | 0.10 | -0.09 – 0.28 | 0.308 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.352 / 0.339 | ||
Climate change concern
| mean_valence_macro_t2 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -1.49 | -2.06 – -0.93 | <0.001 |
| climate concern | 0.32 | 0.23 – 0.41 | <0.001 |
| age | 0.00 | -0.01 – 0.01 | 0.573 |
| gender [weiblich] | 0.07 | -0.16 – 0.31 | 0.547 |
| education [linear] | -0.14 | -0.34 – 0.06 | 0.169 |
| education [quadratic] | 0.11 | -0.09 – 0.32 | 0.276 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.183 / 0.167 | ||
Political orientation
| mean_valence_macro_t2 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.71 | 0.20 – 1.22 | 0.007 |
| politicalOrientation | -0.14 | -0.20 – -0.07 | <0.001 |
| age | 0.00 | -0.01 – 0.01 | 0.528 |
| gender [weiblich] | 0.05 | -0.20 – 0.30 | 0.697 |
| education [linear] | -0.15 | -0.36 – 0.07 | 0.182 |
| education [quadratic] | 0.20 | -0.02 – 0.41 | 0.080 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.079 / 0.060 | ||
specification under the curve analysis emotional properties
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
Standardized
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
Standardized climate concern
FALSE Scale for fill is already present.
FALSE Adding another scale for fill, which will replace the existing scale.
Hypothesis 2
Latent properties
We will explore differences in latent properties of mental models of [those intending to vote yes versus no on the upcoming climate protection law].
Rating of law and climate change concern
Density indicator
Both are close to significance but not quite
| density_macro_t1 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.27 | 0.26 – 0.29 | <0.001 |
| climate concern | 0.01 | -0.00 – 0.02 | 0.074 |
| age | 0.01 | 0.00 – 0.02 | 0.028 |
| gender [weiblich] | -0.01 | -0.03 – 0.01 | 0.360 |
| education [linear] | 0.03 | 0.01 – 0.05 | 0.005 |
| education [quadratic] | 0.00 | -0.02 – 0.02 | 0.726 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.071 / 0.053 | ||
| density_macro_t1 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.27 | 0.26 – 0.29 | <0.001 |
| ratingLaw | 0.01 | -0.00 – 0.02 | 0.093 |
| age | 0.01 | 0.00 – 0.02 | 0.017 |
| gender [weiblich] | -0.01 | -0.03 – 0.01 | 0.366 |
| education [linear] | 0.03 | 0.01 – 0.05 | 0.006 |
| education [quadratic] | 0.00 | -0.02 – 0.02 | 0.702 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.070 / 0.051 | ||
Rating of law and climate change concern
Number of nodes indicatorClimate concern close ~ approaching significance
| num_nodes_macro_t1 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 9.69 | 9.34 – 10.05 | <0.001 |
| climate concern | -0.20 | -0.47 – 0.07 | 0.150 |
| age | -0.31 | -0.59 – -0.04 | 0.024 |
| gender [weiblich] | -0.06 | -0.61 – 0.49 | 0.828 |
| education [linear] | -0.40 | -0.87 – 0.07 | 0.097 |
| education [quadratic] | 0.13 | -0.35 – 0.61 | 0.602 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.039 / 0.019 | ||
| num_nodes_macro_t1 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 9.70 | 9.34 – 10.06 | <0.001 |
| ratingLaw | -0.02 | -0.30 – 0.25 | 0.880 |
| age | -0.31 | -0.59 – -0.03 | 0.028 |
| gender [weiblich] | -0.07 | -0.62 – 0.48 | 0.798 |
| education [linear] | -0.41 | -0.89 – 0.06 | 0.088 |
| education [quadratic] | 0.10 | -0.38 – 0.59 | 0.671 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.031 / 0.011 | ||
specification under the curve analysis latent properties
Rating of law
FALSE Scale for fill is already present.
FALSE Adding another scale for fill, which will replace the existing scale.
Standardized rating of law
FALSE Scale for fill is already present.
FALSE Adding another scale for fill, which will replace the existing scale.
With climate change concern as predictor
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
Hypothesis 3 content
Hypothesis 4
We expect that evaluation of the law will be positively correlated with the emotional properties of mental models. Such that a more positive evaluation of the law will be associated with more positive valence.
Supported| mean_valence_macro_t1 | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.01 | -0.13 – 0.16 | 0.877 |
| ratingLaw | 0.50 | 0.39 – 0.61 | <0.001 |
| age | 0.10 | -0.01 – 0.21 | 0.076 |
| gender [weiblich] | 0.04 | -0.18 – 0.26 | 0.729 |
| education [linear] | -0.28 | -0.48 – -0.09 | 0.004 |
| education [quadratic] | 0.08 | -0.12 – 0.27 | 0.436 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.257 / 0.243 | ||
FALSE `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Longitudinal
Hypothesis 5
We expect to find temporal differences in latent properties of mental models from wave 2 to wave 1. Such that the complexity of mental models will increase from wave 1 to wave 2.
Not supported
| num_nodes_macro | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 9.95 | 8.84 – 11.06 | <0.001 |
| wave [t2] | 0.08 | -0.23 – 0.39 | 0.612 |
| media engagement rel | 0.02 | -0.23 – 0.27 | 0.888 |
| ratingLaw | 0.08 | -0.02 – 0.19 | 0.125 |
| age | -0.02 | -0.04 – -0.00 | 0.017 |
| gender [weiblich] | -0.29 | -0.78 – 0.20 | 0.247 |
|
education [Hochschulreife / Matur] |
0.24 | -0.33 – 0.82 | 0.405 |
|
education [Obligatorische Schule] |
0.59 | -0.02 – 1.20 | 0.058 |
| Random Effects | |||
| σ2 | 3.20 | ||
| τ00 participantID | 2.20 | ||
| ICC | 0.41 | ||
| N participantID | 254 | ||
| Observations | 508 | ||
| Marginal R2 / Conditional R2 | 0.036 / 0.430 | ||
| density_macro | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.23 | 0.18 – 0.27 | <0.001 |
| wave [t2] | 0.01 | -0.01 – 0.02 | 0.232 |
| media engagement rel | 0.01 | -0.00 – 0.02 | 0.168 |
| ratingLaw | 0.00 | -0.00 – 0.01 | 0.442 |
| age | 0.00 | 0.00 – 0.00 | 0.006 |
| gender [weiblich] | 0.01 | -0.02 – 0.03 | 0.602 |
|
education [Hochschulreife / Matur] |
-0.01 | -0.04 – 0.01 | 0.395 |
|
education [Obligatorische Schule] |
-0.03 | -0.06 – -0.00 | 0.029 |
| Random Effects | |||
| σ2 | 0.01 | ||
| τ00 participantID | 0.00 | ||
| ICC | 0.37 | ||
| N participantID | 254 | ||
| Observations | 508 | ||
| Marginal R2 / Conditional R2 | 0.042 / 0.393 | ||
| density_macro | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.24 | 0.20 – 0.27 | <0.001 |
| wave [t2] | 0.01 | -0.01 – 0.02 | 0.225 |
| media engagement rel | 0.01 | -0.00 – 0.02 | 0.179 |
| climate concern rel | 0.01 | -0.00 – 0.02 | 0.230 |
| age | 0.00 | 0.00 – 0.00 | 0.007 |
| gender [weiblich] | 0.01 | -0.02 – 0.03 | 0.624 |
|
education [Hochschulreife / Matur] |
-0.01 | -0.03 – 0.01 | 0.422 |
|
education [Obligatorische Schule] |
-0.03 | -0.06 – -0.00 | 0.030 |
| Random Effects | |||
| σ2 | 0.01 | ||
| τ00 participantID | 0.00 | ||
| ICC | 0.37 | ||
| N participantID | 254 | ||
| Observations | 508 | ||
| Marginal R2 / Conditional R2 | 0.044 / 0.394 | ||
H5a: This effect may be moderated by engagement with the topic. Such that those that spend more time researching or discussing the topic show an even higher increase in complexity of mental models.
Not supported
| density_macro | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.24 | 0.20 – 0.27 | <0.001 |
| wave [t2] | 0.01 | -0.01 – 0.02 | 0.226 |
| media engagement rel | 0.01 | -0.00 – 0.02 | 0.159 |
| climate concern rel | 0.01 | -0.00 – 0.02 | 0.230 |
| age | 0.00 | 0.00 – 0.00 | 0.007 |
| gender [weiblich] | 0.01 | -0.02 – 0.03 | 0.624 |
|
education [Hochschulreife / Matur] |
-0.01 | -0.03 – 0.01 | 0.422 |
|
education [Obligatorische Schule] |
-0.03 | -0.06 – -0.00 | 0.030 |
|
wave [t2] × media engagement rel |
-0.00 | -0.02 – 0.01 | 0.603 |
| Random Effects | |||
| σ2 | 0.01 | ||
| τ00 participantID | 0.00 | ||
| ICC | 0.36 | ||
| N participantID | 254 | ||
| Observations | 508 | ||
| Marginal R2 / Conditional R2 | 0.044 / 0.393 | ||
Specification curve analysis longitdunal
FALSE Scale for fill is already present.
FALSE Adding another scale for fill, which will replace the existing scale.
Hypothesis 6
H6: We expect temporal changes such that emotional properties will become more extreme over time (higher or lower valence). We expect an interaction effect as a function of [intended vote]. Such that [yes (no) intention-voters] will show an increase (decrease) in positive valence.
Not supported (p=.145)
| mean_valence_macro | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -0.35 | -0.74 – 0.05 | 0.087 |
| wave [t2] | -0.09 | -0.27 – 0.09 | 0.315 |
| intendedVote [1] | 0.27 | 0.04 – 0.49 | 0.020 |
| climate concern rel | 0.29 | 0.18 – 0.40 | <0.001 |
| media engagement rel | 0.03 | -0.07 – 0.13 | 0.516 |
| age | 0.00 | -0.00 – 0.01 | 0.246 |
| gender [weiblich] | 0.06 | -0.13 – 0.26 | 0.522 |
|
education [Hochschulreife / Matur] |
0.03 | -0.20 – 0.25 | 0.829 |
|
education [Obligatorische Schule] |
0.28 | 0.03 – 0.52 | 0.027 |
|
wave [t2] × intendedVote [1] |
0.17 | -0.06 – 0.39 | 0.145 |
| Random Effects | |||
| σ2 | 0.32 | ||
| τ00 participantID | 0.44 | ||
| ICC | 0.58 | ||
| N participantID | 254 | ||
| Observations | 508 | ||
| Marginal R2 / Conditional R2 | 0.198 / 0.659 | ||
Exploratory: Media engagement by intended vote
To be discussed: The media engagment item was phrased as: How did you
inform yourself about the climate law and the vote in the past
month this may in itself be a temproal effect?
Not significant relationship, but approaches significance (p =.055)
| mean_valence_macro | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -0.43 | -0.82 – -0.04 | 0.030 |
| wave [t2] | 0.02 | -0.08 – 0.12 | 0.650 |
| media engagement rel | -0.13 | -0.30 – 0.05 | 0.152 |
| intendedVote [1] | 0.35 | 0.16 – 0.54 | <0.001 |
| climate concern rel | 0.31 | 0.19 – 0.42 | <0.001 |
| age | 0.00 | -0.00 – 0.01 | 0.213 |
| gender [weiblich] | 0.04 | -0.16 – 0.24 | 0.694 |
|
education [Hochschulreife / Matur] |
0.06 | -0.18 – 0.29 | 0.637 |
|
education [Obligatorische Schule] |
0.28 | 0.04 – 0.52 | 0.024 |
|
wave [t2] × media engagement rel |
0.06 | -0.04 – 0.16 | 0.263 |
|
media engagement rel × intendedVote [1] |
0.18 | -0.00 – 0.35 | 0.055 |
| Random Effects | |||
| σ2 | 0.32 | ||
| τ00 participantID | 0.44 | ||
| ICC | 0.57 | ||
| N participantID | 254 | ||
| Observations | 508 | ||
| Marginal R2 / Conditional R2 | 0.202 / 0.660 | ||
Hypothesis 7
Number of ambivalent concepts is 0 for both waves…
##
## t1 t2 wave2_control
## 0 113 146 41
## 1 77 61 20
## 2 45 29 5
## 3 9 12 2
## 4 6 2 0
## 5 3 4 2
## 6 0 1 0
## 7 0 1 1
## 8 3 0 1##
## Paired t-test
##
## data: survey_t1_CAM$num_nodes_ambi_macro_t1 and survey_t2_CAM$num_nodes_ambi_macro_t2
## t = 2.5124, df = 253, p-value = 0.01261
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## 0.05020592 0.41436101
## sample estimates:
## mean difference
## 0.2322835##
## Wilcoxon signed rank test with continuity correction
##
## data: survey_t1_CAM$num_nodes_ambi_macro_t1 and survey_t2_CAM$num_nodes_ambi_macro_t2
## V = 5597.5, p-value = 0.008163
## alternative hypothesis: true location shift is not equal to 0